Evidence map›Paper›PMID 39571045›Full record

ArticleNursing open2024

Concurrent Trajectories of Objectively Measured Insufficient Recovery and Workload Among a Cohort of Shift Working Hospital Employees: Quantitative Empirical Research.

Annina Ropponen, Maria Hirvonen, Tero Kuusi, Mikko Härmä

Abstract read
In one paragraph

Article in Nursing open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Annina RopponenFinnish Institute of Occupational Health, Helsinki, Finland.ORCID 0000-0003-3031-5823
Maria HirvonenFinnish Institute of Occupational Health, Helsinki, Finland.
Tero KuusiETLA Economic Research, Helsinki, Finland.
Mikko HärmäFinnish Institute of Occupational Health, Helsinki, Finland.

Funding

Työsuojelurahasto 200103
6 · The paper itself

Abstract

aimTo investigate concurrent changes in short shift intervals (< 11 h) and workload among hospital employees. DESIGN AND DATA SOURCES: This cohort study of 1904 employees in one hospital district in Finland utilised data on employees' working hours for short shift intervals and workload based on the patient classifications aggregated to a 3-week period level across 2 years, 2018-2019. The data was analysed by group-based trajectory modelling and multinominal regression models.

resultsThe seven trajectories model had the best fit to the data-Group 1: very few short shift intervals that are decreasing and low workload (15.0%); Group 2: a low amount of short shift intervals that are decreasing and stable low workload (14.2%); Group 3: moderate amount of short shift intervals that are slightly increasing and low workload (25.1%); Group 4: a low amount of short shift intervals that are slightly decreasing and stable low workload that is slightly increasing (12.1%): Group 5: a moderate amount of both short shift intervals and workload (19.8%): Group 6: short shift intervals that are clearly decreasing, with higher than the average workload decreasing (5.6%); Group 7: moderate amount of short shift intervals and very high workload (8.3%).

conclusionsOnly a minority of hospital employees were found to have both high workloads and insufficient recovery possibilities, but the time-related increases in objective workload were not compensated by better recovery possibilities in working hours. For shift scheduling, it is noteworthy that older employees might seek to work at units in which the workload is lower, which could be considered to support workability. REPORTING

methodRecord. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.

Indexed as

WorkloadAdultCohort StudiesEmpirical ResearchFemaleFinlandHospitalsHumansMaleMiddle AgedPersonnel, HospitalPersonnel Staffing and SchedulingShift Work ScheduleWork Schedule Tolerance

Identifiers

PMID39571045
PMCPMC11580809

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.